1. Field of the Invention
The present invention relates to systems and methods for analyzing medical images, and, more particularly, to systems and methods for analyzing digital mammograms.
2. Description of Related Art
Many computer-aided diagnosis (CAD) schemes have been devised for mammographic image analysis [1-27]. A general review of digital radiography has been given by Doi et al. [1]. Many of these methods are based on multiresolution techniques.
Work related to the use of various multiresolution methods for investigating mammograms includes Refs. 3, 11, 12, 19, 23, and 26. Dengler et al. [11] use a difference of two Gaussians for the detection filter, and the final detection is based on a global threshold. Valatx et al. [12] generate a smooth approximation of the image with a .beta.-spline expansion and apply a mixed distribution based local thresholding technique to both the raw and approximated image; the output image is formed by subtracting the two thresholded images. A calcification segmentation method is developed by Qian et al. [3] using two-channel and multichannel wavelet transforms [19], based on subband selection and a rescaling (thresholding) technique for feature detection [24]. Strickland and Hann [23] apply the wavelet transform at full resolution (no downsampling) and detect independently in two sets (HH and LH+HL) of three full resolution subband images. The detection results are combined, further processed, and the inverse wavelet transform is implemented. De Vore et al. [26] implement the standard wavelet transform, select the important subbands, and invert the transform after wavelet coefficient suppression. The resulting image is empirically thresholded in order to remove the remaining background information.
Various statistical approaches have been used to study mammograms [12-14, 18,21,23,27]. Wavelet domain coefficient probability modeling has also been utilized in other areas of research: selecting optimized coding methods [28, 29], Gauss-Markov field representation [30-32], and texture identification [32].
It is known that film grain noise in mammograms is signal dependent [33, 34]. Typically, the accepted noise field for radiographs results from three independent components: (1) spatial fluctuations in the number of x-ray quanta absorbed in the screen; (2) spatial fluctuations in the screen absorption associated with random structural inhomogeneities in the phosphor coating; and (3) spatial fluctuations in film sensitivity due to the silver halide random distribution per unit area in the emulsion [35]. Many CAD methods have found it essential to carefully treat the image noise with a preprocessing step [3, 15, 22, 27, 36].